ProTherm and ProNIT: thermodynamic databases for proteins and protein–nucleic acid interactions

By M. D. S. Kumar, K. Bava, M. Michael Gromiha, P. Prabakaran, K. Kitajima, H. Uedaira, Akinori Sarai. Nucleic Acids Res., 2005

413 citations recorded by the source corpus.

ProTherm and ProNIT are two thermodynamic databases that contain experimentally determined thermodynamic parameters of protein stability and protein–nucleic acid interactions, respectively. The current versions of both the databases have considerably increased the total number of entries and enhanced search interface with added new fields, improved search, display and sorting options. As on September 2005, ProTherm release 5.0 contains 17 113 entries from 771 proteins, retrieved from 1497 scientific articles (∼20% increase in data from the previous version). ProNIT release 2.0 contains 4900 entries from 273 research articles, representing 158 proteins. Both databases can be queried using WWW interfaces. Both quick search and advanced search are provided on this web page to facilitate easy retrieval and display of the data from these databases. ProTherm is freely available online at and ProNIT at .

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5488918acca42d0098e04d7083340fb9fc5fef84

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Citations in the corpus

A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design (2015, methodology): “associated with experimental values taken from the literature—which we were able to determine using the rich source of data provided by the ProTherm database [7]—so that outliers in the predicted set can be investigated using the original experimental data.”
Inferring Stabilizing Mutations from Protein Phylogenies: Application to Influenza Hemagglutinin (2009, background): “One attractive aspect of this approach is that there is a basis for specifying a meaningful prior over G, since DDG values can be measured experimentally [53,74], or more easily predicted with at least mild accuracy by one of the available physicochemical modeling programs [1–8].”
From sequence to dynamics: the effects of transcription factor and polymerase concentration changes on activated and repressed promoters (2009, positive): “coli TFs with variations of their binding sequences from the ProNIT database [21].”
Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics (2014, background): “(1), DGwildtype and DDG, have been established experimentally [27].”
SPROUTS: a database for the evaluation of protein stability upon point mutation (2008, methodology): “The evaluation of the five tools is accomplished by reference to Protherm (3) the only database providing experimental data.”
Role of conformational sampling in computing mutation-induced changes in protein structure and stability (2010, positive): “8 Duplicate entries were resolved by taking the highest resolution structure, and if multiple experimental measurements were recorded, the mean of all reported measurements was used.”
DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach (2014, methodology): “DUET’s regression model was trained on data for mutations derived from the ProTherm database (18) and used in a previous study (15).”
DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability (2018, methodology): “In this work, we used the previously established S2648 dataset (15–18), derived from the ProTherm database (19).”
Predict impact of single amino acid change upon protein structure (2012, background): “The second consisted of 657 mutants having an effect on protein stability and 652 mutants with no effect on stability covered by 47 proteins [19,20].”
mCSM: predicting the effects of mutations in proteins using graph-based signatures (2013, methodology): “We have also applied our methodology to predict changes of stability resulting from mutations occurring in the tumour suppressor protein p53. mCSM outperforms other methods, demonstrating its applicability to understanding mutations that lead to disease.”
SDM: a server for predicting effects of mutations on protein stability (2017, methodology): “The first data set, S2648 (17), derived from the ProTherm database (22), comprises 2648 single-point mutations in 131 different globular proteins.”
A biophysical protein folding model accounts for most mutational fitness effects in viruses (2011, methodology): “These qualitative features agree with previous work (analytic and simulation) (18, 27, 28) as well as the empirical distribution sampled from different genes and organisms in all kingdoms of life obtained from the ProTherm database (20).”
DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations (2020, methodology): “39 48 These were then used to train and test machine learning algorithms.”